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accelerate-0.21.0\n"]}],"source":["!pip install transformers==4.28.0\n","!pip install datasets\n","!pip install accelerate -U"]},{"cell_type":"code","source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1689736863903,"user_tz":420,"elapsed":6976,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"55af7361-9bae-4782-b1a2-c381470f9c4e"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:02<00:00, 24.3MB/s]\n"]}]},{"cell_type":"code","source":["# the following lines (177 in total) are directly copied from Haffner et al. (2023), Political Analysis\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 20\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    for param in model.bert.encoder.parameters():\n","        param.requires_grad = False\n","    for param in model.bert.embeddings.parameters():\n","        param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")\n","\n","trainer.train()\n","\n","for obj in trainer.state.log_history:\n","    print(obj)\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()\n","\n","raw_pred_test, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred_test[:20])\n","pd.DataFrame(raw_pred_test).to_csv('/data/cw_pred_test_zero.csv')\n","\n","df_results = pd.DataFrame(columns=['Test_MSE','Test_R2'], index=range(0,1))\n","mse = mean_squared_error(ds['test']['fatalities_log'], raw_pred_test)\n","r_squared = r2_score(ds['test']['fatalities_log'], raw_pred_test)\n","print(mse)\n","print(r_squared)\n","df_results['Test_MSE'][0] = mse\n","df_results['Test_R2'][0] = r_squared\n","df_results.to_csv('/data/bert_metrics.csv', index=False)\n","\n","## Whole CW\n","print('Now whole corpus')\n","raw_train1_ds = Dataset.from_pandas(final)\n","ds = {\"test\": raw_train1_ds}\n","\n","## Create dataset\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","print('Now predicting')\n","raw_pred, _, _ = trainer.predict(ds[\"test\"])\n","print(raw_pred[:20])\n","pd.DataFrame(raw_pred).to_csv('/data/cw_bert_pred_zero.csv')\n","\n","print('DF saved')\n","\n","trainer.eval_dataset=ds[\"test\"]\n","trainer.evaluate()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["9d591facd42c4dbdbe23e71210dc5384","fb4447488bdb4c709f4c78d937f6edea","3143981c28b942e69c50c2c978c98259","6d6c776267b841f1b4e3e12c93bd9fb0","2a72e4ecfc1746518d02564ca0730eb2","b148de58da334caea160632a545639e8","19ca2a0fb0e74834838978243ed4b93f","19a1b271c7084f298353819b6d82f48d","41620e9e893b42e6bf2d4cc298afcc91","73666bdc71a04e178320e21e13c159f9","e809ebe210164e52ac127f9506679101","b63eeabb4da5476e8db5109c75927651","45aa31e2467f4d36ab17daafb598e721","6755af538f18430992277bdd938fcdd2","9845f99654cf487ca6c410b5c6f6e17e","6820f37fff6f487f9fdbb5b8dac00e6c","9ccbf98a3fc3418fb09b40f3421e68b0","dc7bcbed056e491692050ac05153e0c7","9431ab04ff6c40c7a629eee7b2706ed2","77570855f668441081153192d775badb","83a02c60936c46a2be32632184b44770","0c2d884b4bce4d7aad0bf53d3a3ea4f2","5674307540444f138f3c511bfc38bc9f","754c646c42ee4b2c8ee0f9f539056868","eb7a66933da642b98d08be9930dace71","f5788e116e4d4593ae2d12c8d28f9f12","fee5b0294ab14550ac3d06c36f08c6f4","b0503990f984480f83aa5d821a08b0e6","29876a1e541f4b19ac27bc98af64ba94","f3c11b0013a04e3d92d21d2e295d3f0a","5819eedb30a4485b99ada23f68715232","973010a244cf4c2e8445f787e71fadd3","e7810aa2ea9541e4a81e58514a559087","8e1ebd52b68a4334b31a59fad54a29bd","85ab7010208040d5bfb5be6d3310edb0","ddb332c4a53f4d27b16ecc0c6be819f8","f8a66065e4a040288c3a3bd23b8c0b9f","580d2085134a4f4884fea0db52beb09c","147dab68842540caaa23b62966c40bfd","f5cde540659341f1aa8222bbc369173b","ef4ca28b663f4995888534c82885a322","67b0d4f3b86945ad8cd2486810318ec8","5de9fdbd955446f4beb0a4cad202b102","ed3ec6e41d7d471f91b46d0ccd65c11b","ac19f10fa144495883879e9617888c4d","72314c475a9d45709188ee1082a6d80d","75c7d7387c0a4542950095175e098f7d","b3f88e73432648d2933fc214752d9a3e","21d337630e1f4ffd8cadf4d8535ebc5b","f7cd93cb42b64b11abdebc9334226704","b0b619d7d11b4d84be96f4bccd962026","ad6eb48e1717462885aadeb887f5d190","1301c4d12b7d4e98b7469bef9f239323","c08bbdbdf5854410811da1dbb040e30c","dc64569279f9472fa95b85cc010aaada","bb014dafca4b4a1b943bac729027d80d","0e8b67ad7923463a827630eca3b48e62","a80d86bc411f4955b07a66a76af23366","7012eff1ced543efb6f3410e429dab94","4feaf53ce403459bae85af8deba6c0db","f1a73b9930dd438f8a3d22639ef9cfe7","ebcb0c27fefb4578930e7d2b98b58c4d","1ee43a08c1394b22bf5b18bb8d348f71","9ac9e3f3840f495cb86dcf301271bc1d","c15e06cb7b8944cca6d97cf702e7dd8c","728f3f18434149c8957a201282435779","723cfd7964f9494b932b2cb806fccdcc","1d0ef3fc0ce042c3aa992c751647fc34","455e43310efe435da6bae827135fad21","7602a3658d454a7c8049002ea2dd761f","b5cae175b4e84a6a9293a1f2bd34e879","c8d61bd1b462451f941ae83d9fe9b131","b7b070a02aa5444b9779c7ac762308ee","3efd89610f624140b698412f93066258","895dd3eab0c24424b5917c4dba59c99a","4206a3d00b674dd59ad472b71565907b","1623de635289439db1c12fd5bf51fbbf","50186a510ce640d8a0e499ec26e3707f","a41d758b70ac4e7e9487ea5e6d03a05d","c88b86b6d2774beaaf84ba2f63d1f409","907652372c504ce29ea4393a991e389a","00d3d900fdf2443791c8ee0d6cebb05f","f2974c5d7f454bec88c3f6408d89b4c9","cda7bf279183495db9336d78e1cf9caf","d9f2e5dee5bf469ca352de34f26fbdca","25473bde9fcd4067b24d740ace9e2197","662dd740388848ce83fa8810f53dbfa6","1ad4abe48f9f492d94ac0ea1ff7fba72","5387497bb2064e869f4914ecd42b3de9","dca28d4d86ba4f1587ea1ad8e72522c6","c09fc0deecd2411dbdecca50e9aa01f6","0132c0239b6449fc8b9d9f01684f2b38","a0e14cda46a442c8be15fb5ba9b5684c","706050c12f594c0faa4559b7a5a35070","78b5202347fb4faa95cf8167ce3bb984","0f3dacedf3144dbeb36e9ba10487a585","ab8b29fa4cd8488a888d6b84c3dc5d65","e563560540ca4510b4b0307cd83f2b82","cbba708e42764fd788727e2afd65ce74","3f9db72c518e4e61be11bc353d37ef0d","4b647ee2b4974514aa46f5c62705051e","9af5b89cb1fe47dc966fa572ec430c77","f3d3284c8b8b42b094fcc8ae27293982","ea193531a3514116a9d0927fa036b7b4","0a5423f21f7f4cdf8d486a4ffb600af1","5397f2a264f14dc0a36e93c40f3ebb90","a903d562af354729b8ff95b330ea6dcb","762714af15ba43bab74f8d4fd5a32d38","7bc6527bf75e494fbb4fe086c02c052e","2a84a591cbf246d7b9da0f324cd368d9"]},"id":"_UUtlbRAySQ_","executionInfo":{"status":"ok","timestamp":1689739112847,"user_tz":420,"elapsed":2248950,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"98fe6b8c-8875-4416-9b9b-212c17532fa7"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-d395000a7f50>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-d395000a7f50>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-d395000a7f50>:28: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-d395000a7f50>:34: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-d395000a7f50>:32: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:07<00:00, 3206.12it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d591facd42c4dbdbe23e71210dc5384"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b63eeabb4da5476e8db5109c75927651"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5674307540444f138f3c511bfc38bc9f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e1ebd52b68a4334b31a59fad54a29bd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac19f10fa144495883879e9617888c4d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bb014dafca4b4a1b943bac729027d80d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/648 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"723cfd7964f9494b932b2cb806fccdcc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1296 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"50186a510ce640d8a0e499ec26e3707f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/437M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5387497bb2064e869f4914ecd42b3de9"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['bert.pooler.dense.weight', 'classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['bert.pooler.dense.weight', 'classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='27420' max='27420' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [27420/27420 33:08, Epoch 20/20]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.769100</td>\n","      <td>1.872889</td>\n","      <td>1.872889</td>\n","      <td>0.858117</td>\n","      <td>0.521855</td>\n","      <td>0.555556</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.598100</td>\n","      <td>1.835728</td>\n","      <td>1.835728</td>\n","      <td>0.903448</td>\n","      <td>0.531342</td>\n","      <td>0.498457</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.516700</td>\n","      <td>1.672589</td>\n","      <td>1.672589</td>\n","      <td>0.805663</td>\n","      <td>0.572991</td>\n","      <td>0.584877</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.449900</td>\n","      <td>1.946729</td>\n","      <td>1.946729</td>\n","      <td>0.951706</td>\n","      <td>0.503004</td>\n","      <td>0.257716</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.422500</td>\n","      <td>1.716329</td>\n","      <td>1.716329</td>\n","      <td>0.864610</td>\n","      <td>0.561825</td>\n","      <td>0.570988</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.446400</td>\n","      <td>1.566173</td>\n","      <td>1.566172</td>\n","      <td>0.805012</td>\n","      <td>0.600159</td>\n","      <td>0.550926</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.508100</td>\n","      <td>2.116416</td>\n","      <td>2.116416</td>\n","      <td>1.030933</td>\n","      <td>0.459683</td>\n","      <td>0.229938</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.354400</td>\n","      <td>1.673314</td>\n","      <td>1.673314</td>\n","      <td>0.835069</td>\n","      <td>0.572806</td>\n","      <td>0.600309</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>1.287500</td>\n","      <td>1.691272</td>\n","      <td>1.691272</td>\n","      <td>0.795619</td>\n","      <td>0.568222</td>\n","      <td>0.617284</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>1.464700</td>\n","      <td>2.009888</td>\n","      <td>2.009888</td>\n","      <td>0.932803</td>\n","      <td>0.486880</td>\n","      <td>0.527778</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>1.421800</td>\n","      <td>1.631290</td>\n","      <td>1.631290</td>\n","      <td>0.817433</td>\n","      <td>0.583535</td>\n","      <td>0.529321</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>1.283600</td>\n","      <td>1.740021</td>\n","      <td>1.740021</td>\n","      <td>0.910729</td>\n","      <td>0.555776</td>\n","      <td>0.509259</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>1.357100</td>\n","      <td>1.749684</td>\n","      <td>1.749684</td>\n","      <td>0.989918</td>\n","      <td>0.553309</td>\n","      <td>0.131173</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>1.314900</td>\n","      <td>2.249039</td>\n","      <td>2.249039</td>\n","      <td>1.182870</td>\n","      <td>0.425825</td>\n","      <td>0.120370</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>1.333600</td>\n","      <td>1.562594</td>\n","      <td>1.562594</td>\n","      <td>0.740488</td>\n","      <td>0.601073</td>\n","      <td>0.591049</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>1.339500</td>\n","      <td>1.588174</td>\n","      <td>1.588174</td>\n","      <td>0.881234</td>\n","      <td>0.594542</td>\n","      <td>0.504630</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>1.293100</td>\n","      <td>1.613602</td>\n","      <td>1.613602</td>\n","      <td>0.747655</td>\n","      <td>0.588051</td>\n","      <td>0.549383</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>1.268300</td>\n","      <td>1.746420</td>\n","      <td>1.746420</td>\n","      <td>0.962329</td>\n","      <td>0.554142</td>\n","      <td>0.188272</td>\n","    </tr>\n","    <tr>\n","      <td>9500</td>\n","      <td>1.268500</td>\n","      <td>1.588716</td>\n","      <td>1.588716</td>\n","      <td>0.807190</td>\n","      <td>0.594404</td>\n","      <td>0.527778</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>1.220400</td>\n","      <td>1.520985</td>\n","      <td>1.520985</td>\n","      <td>0.724390</td>\n","      <td>0.611696</td>\n","      <td>0.609568</td>\n","    </tr>\n","    <tr>\n","      <td>10500</td>\n","      <td>1.265400</td>\n","      <td>1.440764</td>\n","      <td>1.440764</td>\n","      <td>0.788061</td>\n","      <td>0.632176</td>\n","      <td>0.564815</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>1.272200</td>\n","      <td>1.485920</td>\n","      <td>1.485920</td>\n","      <td>0.744357</td>\n","      <td>0.620648</td>\n","      <td>0.541667</td>\n","    </tr>\n","    <tr>\n","      <td>11500</td>\n","      <td>1.254600</td>\n","      <td>1.554140</td>\n","      <td>1.554140</td>\n","      <td>0.741160</td>\n","      <td>0.603231</td>\n","      <td>0.541667</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>1.236700</td>\n","      <td>1.466922</td>\n","      <td>1.466922</td>\n","      <td>0.685849</td>\n","      <td>0.625498</td>\n","      <td>0.623457</td>\n","    </tr>\n","    <tr>\n","      <td>12500</td>\n","      <td>1.176800</td>\n","      <td>1.470245</td>\n","      <td>1.470245</td>\n","      <td>0.693997</td>\n","      <td>0.624649</td>\n","      <td>0.600309</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>1.164100</td>\n","      <td>1.460863</td>\n","      <td>1.460863</td>\n","      <td>0.757223</td>\n","      <td>0.627045</td>\n","      <td>0.594136</td>\n","    </tr>\n","    <tr>\n","      <td>13500</td>\n","      <td>1.177200</td>\n","      <td>1.655552</td>\n","      <td>1.655552</td>\n","      <td>0.947994</td>\n","      <td>0.577341</td>\n","      <td>0.151235</td>\n","    </tr>\n","    <tr>\n","      <td>14000</td>\n","      <td>1.204000</td>\n","      <td>1.402696</td>\n","      <td>1.402696</td>\n","      <td>0.677243</td>\n","      <td>0.641894</td>\n","      <td>0.586420</td>\n","    </tr>\n","    <tr>\n","      <td>14500</td>\n","      <td>1.175300</td>\n","      <td>1.427544</td>\n","      <td>1.427544</td>\n","      <td>0.681085</td>\n","      <td>0.635551</td>\n","      <td>0.629630</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>1.136400</td>\n","      <td>1.532895</td>\n","      <td>1.532895</td>\n","      <td>0.775673</td>\n","      <td>0.608655</td>\n","      <td>0.584877</td>\n","    </tr>\n","    <tr>\n","      <td>15500</td>\n","      <td>1.110500</td>\n","      <td>1.517850</td>\n","      <td>1.517850</td>\n","      <td>0.806701</td>\n","      <td>0.612496</td>\n","      <td>0.557099</td>\n","    </tr>\n","    <tr>\n","      <td>16000</td>\n","      <td>1.148400</td>\n","      <td>1.402709</td>\n","      <td>1.402709</td>\n","      <td>0.679270</td>\n","      <td>0.641891</td>\n","      <td>0.631173</td>\n","    </tr>\n","    <tr>\n","      <td>16500</td>\n","      <td>1.146400</td>\n","      <td>1.583846</td>\n","      <td>1.583846</td>\n","      <td>0.899560</td>\n","      <td>0.595647</td>\n","      <td>0.432099</td>\n","    </tr>\n","    <tr>\n","      <td>17000</td>\n","      <td>1.127700</td>\n","      <td>1.434557</td>\n","      <td>1.434557</td>\n","      <td>0.730570</td>\n","      <td>0.633761</td>\n","      <td>0.645062</td>\n","    </tr>\n","    <tr>\n","      <td>17500</td>\n","      <td>1.071800</td>\n","      <td>1.409362</td>\n","      <td>1.409362</td>\n","      <td>0.640301</td>\n","      <td>0.640193</td>\n","      <td>0.634259</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>1.101300</td>\n","      <td>1.520887</td>\n","      <td>1.520887</td>\n","      <td>0.765387</td>\n","      <td>0.611721</td>\n","      <td>0.662037</td>\n","    </tr>\n","    <tr>\n","      <td>18500</td>\n","      <td>1.158000</td>\n","      <td>1.476182</td>\n","      <td>1.476182</td>\n","      <td>0.716755</td>\n","      <td>0.623134</td>\n","      <td>0.652778</td>\n","    </tr>\n","    <tr>\n","      <td>19000</td>\n","      <td>1.068800</td>\n","      <td>1.456014</td>\n","      <td>1.456014</td>\n","      <td>0.729723</td>\n","      <td>0.628283</td>\n","      <td>0.615741</td>\n","    </tr>\n","    <tr>\n","      <td>19500</td>\n","      <td>1.108400</td>\n","      <td>1.377729</td>\n","      <td>1.377729</td>\n","      <td>0.754323</td>\n","      <td>0.648268</td>\n","      <td>0.618827</td>\n","    </tr>\n","    <tr>\n","      <td>20000</td>\n","      <td>1.087500</td>\n","      <td>1.399716</td>\n","      <td>1.399716</td>\n","      <td>0.665178</td>\n","      <td>0.642655</td>\n","      <td>0.625000</td>\n","    </tr>\n","    <tr>\n","      <td>20500</td>\n","      <td>1.082800</td>\n","      <td>1.444070</td>\n","      <td>1.444070</td>\n","      <td>0.779879</td>\n","      <td>0.631332</td>\n","      <td>0.591049</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>1.069200</td>\n","      <td>1.451849</td>\n","      <td>1.451849</td>\n","      <td>0.829895</td>\n","      <td>0.629346</td>\n","      <td>0.530864</td>\n","    </tr>\n","    <tr>\n","      <td>21500</td>\n","      <td>1.082000</td>\n","      <td>1.369087</td>\n","      <td>1.369087</td>\n","      <td>0.672870</td>\n","      <td>0.650475</td>\n","      <td>0.628086</td>\n","    </tr>\n","    <tr>\n","      <td>22000</td>\n","      <td>0.988300</td>\n","      <td>1.384007</td>\n","      <td>1.384007</td>\n","      <td>0.671212</td>\n","      <td>0.646666</td>\n","      <td>0.671296</td>\n","    </tr>\n","    <tr>\n","      <td>22500</td>\n","      <td>1.024600</td>\n","      <td>1.354864</td>\n","      <td>1.354864</td>\n","      <td>0.625064</td>\n","      <td>0.654106</td>\n","      <td>0.663580</td>\n","    </tr>\n","    <tr>\n","      <td>23000</td>\n","      <td>1.003300</td>\n","      <td>1.452324</td>\n","      <td>1.452324</td>\n","      <td>0.789585</td>\n","      <td>0.629225</td>\n","      <td>0.594136</td>\n","    </tr>\n","    <tr>\n","      <td>23500</td>\n","      <td>1.065800</td>\n","      <td>1.466174</td>\n","      <td>1.466174</td>\n","      <td>0.814715</td>\n","      <td>0.625689</td>\n","      <td>0.591049</td>\n","    </tr>\n","    <tr>\n","      <td>24000</td>\n","      <td>1.018000</td>\n","      <td>1.401325</td>\n","      <td>1.401325</td>\n","      <td>0.649619</td>\n","      <td>0.642244</td>\n","      <td>0.638889</td>\n","    </tr>\n","    <tr>\n","      <td>24500</td>\n","      <td>1.032300</td>\n","      <td>1.352265</td>\n","      <td>1.352264</td>\n","      <td>0.694420</td>\n","      <td>0.654770</td>\n","      <td>0.611111</td>\n","    </tr>\n","    <tr>\n","      <td>25000</td>\n","      <td>1.021800</td>\n","      <td>1.370081</td>\n","      <td>1.370081</td>\n","      <td>0.642700</td>\n","      <td>0.650221</td>\n","      <td>0.652778</td>\n","    </tr>\n","    <tr>\n","      <td>25500</td>\n","      <td>1.058000</td>\n","      <td>1.389953</td>\n","      <td>1.389953</td>\n","      <td>0.681371</td>\n","      <td>0.645148</td>\n","      <td>0.638889</td>\n","    </tr>\n","    <tr>\n","      <td>26000</td>\n","      <td>0.976300</td>\n","      <td>1.381139</td>\n","      <td>1.381139</td>\n","      <td>0.679365</td>\n","      <td>0.647398</td>\n","      <td>0.641975</td>\n","    </tr>\n","    <tr>\n","      <td>26500</td>\n","      <td>1.002300</td>\n","      <td>1.391944</td>\n","      <td>1.391944</td>\n","      <td>0.740520</td>\n","      <td>0.644639</td>\n","      <td>0.612654</td>\n","    </tr>\n","    <tr>\n","      <td>27000</td>\n","      <td>0.968400</td>\n","      <td>1.375472</td>\n","      <td>1.375472</td>\n","      <td>0.696756</td>\n","      <td>0.648845</td>\n","      <td>0.637346</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'loss': 1.7691, 'learning_rate': 0.001963530269876003, 'epoch': 0.36, 'step': 500}\n","{'eval_loss': 1.8728891611099243, 'eval_mse': 1.8728891611099243, 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object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[[6.3716264]\n"," [5.681528 ]\n"," [5.7785764]\n"," [5.4358826]\n"," [5.8792214]\n"," [5.486261 ]\n"," [5.73892  ]\n"," [5.5605016]\n"," [6.2828593]\n"," [5.6278005]\n"," [6.443804 ]\n"," [5.4274154]\n"," [6.0676856]\n"," [5.164043 ]\n"," [5.678424 ]\n"," [4.8615956]\n"," [4.547374 ]\n"," [5.2565317]\n"," [5.5441856]\n"," [4.447984 ]]\n","DF saved\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1492' max='1492' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1492/1492 01:30]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.8736447095870972,\n"," 'eval_mse': 0.8736447095870972,\n"," 'eval_mae': 0.5549454092979431,\n"," 'eval_r2': 0.7514670146874632,\n"," 'eval_accuracy': 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